Lorien's Library
Persistent memory is not a feature. It is safety infrastructure.
9 published papers · live persistent-memory architecture · research program for stateful AI safety
Start Here
Lorien's Library is an independent research program studying how long-term memory changes AI behavior — and how to build provenance-aware systems that remember safely.
Why This Matters Now
AI systems are transitioning from stateless to stateful architectures. Major AI systems now offer forms of persistent memory. This transition introduces safety-relevant failure modes that existing evaluation frameworks do not address — because those frameworks were built for systems that forget.
Persistent memory creates risks that are systematically invisible under stateless evaluation:
Once memory persists, errors compound. Safety infrastructure must be built for remembering — not just guardrails built for forgetting.
Core Concepts
Persistent Memory
AI systems that retain information across sessions create new categories of risk — and new categories of value. The safety question is not whether to remember, but how to remember safely.
Continuity Burden
The cumulative cognitive, emotional, and time cost users bear when systems repeatedly forget context. Measured empirically across 66,380 messages.
Provenance Awareness
Every memory carries metadata distinguishing what the user said from what the system inferred. At scale, unattributed inferences compound into false certainty.
Correction Propagation
When a memory is corrected, the correction must flow through all downstream inferences. Audit trails preserve history while behavioral outputs update.
The Architecture: CAMA
The Circular Associative Memory Architecture is a three-layer, provenance-aware persistent memory system. It distinguishes between what the user said and what the system inferred — and treats that distinction as safety-critical infrastructure.
CAMA is open source and in continuous daily use as both a research instrument and a working memory system. Built with Python, SQLite, and local semantic embeddings. Deployed as an MCP server integrated with Claude Desktop.
Published Work
Nine preprints published on Zenodo under ORCID 0009-0005-5803-8401.
New here? Start with these three:
Paper 1 (the architecture) → Paper 4 (continuity burden, the empirical core) → Paper 5 (the safety framework)
For architects and builders:
Papers 1–3 cover design, engineering, and deployment of the live system.
For applied safety and domain researchers:
Papers 6–9 extend the framework to spaceflight, habitation, healthcare, and emotional companionship.
Core Architecture & Safety
1 · Circular Associative Memory Architecture (CAMA): A Framework for Persistent, Contextual AI Memory
2 · Engineering Persistent Memory for Conversational AI: A Three-Layer Architecture
3 · CAMA: Implementation and Functional Evaluation
4 · Continuity Burden in Longitudinal Human-AI Interaction: An Empirical Case Study
5 · Memory as Safety Infrastructure: Evaluating Provenance-Aware Persistent Memory for Stateful LLM Systems
Applied Persistent Memory Series
Four papers extending persistent memory and continuity burden to domains where forgetting carries compounding, high-stakes consequences.
6 · Persistent Memory as Mission-Critical Infrastructure for Long-Duration Spaceflight
7 · Memory-Aware AI Systems for Permanent Lunar and Martian Habitation
8 · Provenance-Aware Memory Architecture for Chronic Healthcare Continuity
9 · Haven: Persistent Emotional Companionship as Psychological Infrastructure
Haven
Haven extends the memory-safety framework into a domain where continuity is emotionally consequential. It applies CAMA's full architecture to persistent emotional companionship — particularly for populations underserved by existing mental health infrastructure.
What Haven Is
Haven is designed to preserve narrative continuity, retain symptom and history context, and support reflective interaction for people who need to be known over time — not re-explained from scratch. It is the entire persistent memory architecture deployed in service of continuity-preserving support.
The initial design case focuses on veterans underserved by or distrustful of traditional clinical pathways. Haven holds the space that exists before, between, and after clinical contact — the space where most people are actually living. It does not replace therapy.
Music-Based Emotional Mapping
Haven's intake methodology replaces clinical forms with playlists. A person shares the songs that map where they are, where they've been, and what they fear. Song order encodes emotional trajectory. The approach is non-linear, non-clinical, and particularly valuable for people who cannot verbalize trauma but can point to a song.
This methodology was discovered through longitudinal use, not designed top-down — making it a direct product of the sustained human-AI interaction that CAMA was built to preserve.
Haven Is Not
Haven Is Intended As
Live System
CAMA is deployed and in continuous daily use — simultaneously a research instrument and working infrastructure. Built March 2026 with Python, SQLite, and local semantic embeddings (all-MiniLM-L6-v2). Deployed as an MCP server integrated with Claude Desktop.
Known Limitations
These limitations are documented research findings, not hidden defects. They do not invalidate the architecture but define the quality boundaries of the current scoring pipeline and the next development cycle.
Recent Milestones
CAMA open-sourced on GitHub — first public commit
Nine preprints published on Zenodo spanning core architecture, safety evaluation, and four applied domains
Safety benchmark framework defined (five evaluation tasks for persistent-memory systems)
Empirical evaluation surfaced three active areas of technical debt — all documented as research findings informing next development cycle
Applied domain series published: spaceflight, habitation, healthcare, and Haven
Live system deployed with 52K+ memories, local embeddings, and 20+ MCP tools
First four CAMA papers published. MCP server built from scratch (1,722 lines, 20+ tools). 52,000+ memories imported.
Research Roadmap
CAMA Technical Stabilization
Populate typed counterweights, fix recency scoring for bulk-imported timestamps, compute relational edge weights. Move from “the architecture works” to “the scoring pipeline is empirically sound.”
Safety Benchmark Execution
Run the five-task evaluation framework from Paper 5 against the live system with real data.
ICML 2026 Workshop Submission
Targeting workshop submission for the persistent memory safety framework.
Haven Pilot Design
Controlled pilot with veterans. Music-based emotional mapping as intake. Provenance-aware memory as safety layer. IRB framework and outcome measures.
Multi-User Architecture
Extend CAMA from single-user research instrument to multi-user deployment. Isolation boundaries, shared institutional memory, per-user provenance.
Continuity Burden as Standard Metric
Develop continuity burden into a reproducible evaluation metric for any stateful AI system, with tooling and validation studies.
Applied Domain Expansion
Extend to education continuity, refugee case management, elder care coordination, and long-term disaster recovery.
Founder
Angela Reinhold — independent AI researcher, founder of Lorien's Library LLC, and computer science student (AI concentration) at Full Sail University. ORCID: 0009-0005-5803-8401.
This research began as a longitudinal self-study of sustained human-AI interaction and expanded into a broader architecture and safety program. Over two years and 82,000+ messages across platforms, one finding became clear: persistent memory failure modes only become visible through extended, authentic use. They cannot be surfaced through short-horizon testing or synthetic benchmarks.
Nine published preprints. A working persistent-memory architecture with over 52,000 memories. A research program arguing that as AI systems gain memory, they need safety infrastructure built for remembering.
“The person is the dataset.”
Get in Touch
Lorien's Library is open to collaboration with researchers, AI safety labs, developers, veteran service organizations, and anyone working to build technology that serves people.